The future of environmental health research will include increased emphasis on biological mechanisms and biomarkers of exposure or latent disease processes. Statistical methods for biomarkers in environmental health research can play an important role in this research, but they have received insufficient attention to date. This application is for a K22 Transition to Independent Environmental Health Research Career Development Award. The candidate proposes a K22 project on statistical methods for modeling the effects of air pollution on exhaled breath biomarkers. This will be the foundational research project in an independent research program in environmental biostatistics, focused on developing and applying statistical methods to gain an integrated understanding of the mechanisms of environmental exposure effects. Advanced analytics of most exhaled biomarkers is still relatively new, but one of the best studied exhaled biomarkers to date is exhaled nitric oxide [FeNO], a biomarker indicative of airway inflammation. This project focuses on modeling FeNO data in the context of air pollution health effects research. However, similar modeling issues have already been identified for components of exhaled breath condensate and exhaled volatile organic compounds, and more will likely be discovered as the field advances. Epidemiologic data have linked air pollution exposure with elevated FeNO, leading to increased risk of incident asthma in children. A newer method of collection at multiple flow rates allows FeNO to be partitioned into airway and alveolar sources by estimating parameters in a two-compartment deterministic model of NO exchange in the lower respiratory system. Existing multiple flow analysis methods were developed for data from well-controlled experimental settings with a small number of participants, but direct applicability of these methods to large- scale epidemiologic studies is questionable. Multiple flow FeNO data collection has been collected in several epidemiologic studies, one of the largest of which is the Southern California Children's Health Study (n=1640). Existing statistical methods employed in epidemiologic studies with multiple flows FeNO use data inefficiently and ignore the uncertainty in the estimation of the parameters. To address these issues, the candidate proposes 3 aims for the K22 project to be completed using simulated data and data from the Children's Health Study. Aim 1 is to develop, evaluate, and apply non-linear mixed models to simultaneously estimate two- compartment model parameters and to relate these parameters to potential determinants (e.g., fine and coarse particulate matter air pollution) while appropriately accounting for the uncertainty in parameter estimation. Aim 2 is to develop, evaluate, and apply Bayesian Markov Chain Monte Carlo methods to directly estimate parameters from the simple and robust two-compartment model and a more complex trumpet-shaped model with axial diffusion, and then to compare the fit of both models to Children's Health Study data. Aim 3 is to determine optimal designs for multiple flow measurement studies. The candidate is a Ph.D. biostatistician, and she will use the bridge period afforded by the K22 award to gain expertise in mathematical modeling of physiologic systems, from a biomedical engineering perspective, which will be necessary: a) to complete this research project and b) to extend and generalize the proposed research project into the next phase of her independent research program. The principal investigator's sponsor for this award is Dr. Frank D. Gilliland, a physician-scientist with a strong track-record in environmental health research, and the multidisciplinary network of advisor/collaborators assembled for this award include experts in biostatistics (Dr. David Conti), environmental biostatistics (Dr. Duncan C. Thomas), biomedical engineering (Dr. David D'Argenio), and computational biology (Dr. Paul Marjoram).